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cs.LG2023
Probabilistic Invariant Learning with Randomized Linear Classifiers
Leonardo Cotta, Gal Yehuda, Assaf Schuster +1
Designing models that are both expressive and preserve known invariances of tasks is an increasingly hard problem. Existing solutions tradeoff invariance for computational or memor…
cs.LG2023★ 6 cited
Benchmarking Neural Network Training Algorithms
George E. Dahl, Frank Schneider, Zachary Nado +22
Training algorithms, broadly construed, are an essential part of every deep learning pipeline. Training algorithm improvements that speed up training across a wide variety of workl…